CN110210092B - Body temperature data processing method and device, storage medium and terminal equipment - Google Patents
Body temperature data processing method and device, storage medium and terminal equipment Download PDFInfo
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Abstract
The invention discloses a body temperature data processing method, which comprises the following steps: acquiring body temperature data of a user, performing spatial registration interpretation processing, and determining elements influencing the accuracy of the body temperature to form influence factors; establishing a three-dimensional coordinate and a standard grid, specifying the influence factors in the standard grid in the three-dimensional coordinate, calculating the distance of the standard grid to each influence factor, and then performing normalization processing to generate grid distance data with the same size as the standard grid; calculating the weight of each influence factor through an algorithm, and calculating the development probability of the temperature cells according to the weight value of each influence factor and by combining the grid density and the influence factors; calculating the development probability of the temperature cells and the obtained body temperature data of the user to obtain deduced accurate data; the invention carries out simulated evolution on the temperature data through the cellular automata technology, fits accurate data and realizes the improvement of the accuracy of the thermal induction non-contact body temperature detection method.
Description
Technical Field
The invention relates to the technical field of data, in particular to a body temperature data processing method and device based on a cellular automaton, a storage medium and terminal equipment.
Background
Most of the existing intelligent body temperature detection methods are thermal induction non-contact body temperature detection, the method can detect the body temperature of a human body through a thermal induction technology under the condition of not contacting the human body, is very intelligent, and avoids unnecessary bacterial infection caused by skin contact.
However, the thermal sensing non-contact body temperature detection method has the disadvantage of inaccurate data measurement, because of the non-contact body temperature detection, the heat conduction is influenced in the air due to the distance length and impurities in the air, and because of the extremely high sensitivity of the temperature, the tiny factors can cause the temperature data to have great deviation, so that the thermal sensing non-contact body temperature detection method has adverse effects in practical application.
Disclosure of Invention
The invention provides a body temperature data processing method, a body temperature data processing device, a storage medium and terminal equipment, which are used for solving the technical problem of inaccurate data measurement in the existing thermal induction non-contact body temperature detection, so that the temperature data is subjected to simulated evolution through a cellular automata technology, accurate data is fitted, and the accuracy of the thermal induction non-contact body temperature detection method is improved.
In order to solve the technical problem, an embodiment of the present invention provides a body temperature data processing method, including:
acquiring body temperature data of a user, performing spatial registration interpretation processing, and determining elements influencing body temperature accuracy to form influencing factors;
establishing a three-dimensional coordinate and a standard grid, specifying the influence factors in the standard grid in the three-dimensional coordinate, calculating the distance of the standard grid to each influence factor, and then performing normalization processing to generate grid distance data with the same size as the standard grid;
calculating the weight of each influence factor through an algorithm, and calculating the development probability of the temperature cells according to the weight value of each influence factor and by combining the grid density and the influence factors;
and calculating the development probability of the temperature cells and the acquired body temperature data of the user to obtain deduced accurate data.
As a preferable scheme, after calculating the distance between the standard grid and each influence factor, performing normalization processing to generate grid distance data having a size consistent with that of the standard grid, includes: normalizing the acquired influence factor data, uniformly resampling the data to consistent resolution, and calculating the spatial distance from the temperature cell to each influence factor according to Euclidean measurement, wherein the calculation formula is as follows:
wherein (x) 0 ，y 0 ) Is the coordinate position of the cell (x) k ，y k ) Is the coordinate position of the impact factor, dis is the calculated Euclidean distance.
As a preferred scheme, the weight algorithm for obtaining each influence factor through calculation is a hierarchical analysis algorithm.
Preferably, the calculating by an algorithm to obtain the weight of each influence factor, and calculating the development probability of the temperature cell according to the weight of each influence factor and by combining the grid density and the influence factor includes:
carrying out weighted summation on each influence factor through the weight values of the influence factors to calculate and develop suitability data;
calculating the cell development density of the neighborhood of the central cell according to the number of cells around the central cell and the density of the standard grid;
and multiplying the development suitability data and the cellular suitability data to obtain the development probability of the temperature cellular.
Preferably, the formula for calculating the development suitability data by weighted summation of the weight values of the influence factors is as follows:
p g ＝b 1 x 1 +b 2 x 2 +…+b k x k +…+b n x n
in the formula, x k Is the kth spatial variable, b k Is a variable x k The weight of (a) is calculated by an analytic hierarchy process, p g For the development suitability of the cells, n is the number of influencing factors.
Preferably, the formula for calculating the cell development density of the neighborhood of the central cell according to the number of cells around the central cell and the density of the standard grid is as follows:
in the formula (I), the compound is shown in the specification,
counting the number of cells which have been converted into temperature cells in a 5 × 5 neighborhood of the ith row and jth column of cells ij at time t, wherein 5 × 5 is the length 5 multiplied by the width 5 of a coordinate space region of the standard grid,
density is developed for the cells.
Preferably, the formula for multiplying the development suitability data and the cell suitability data to obtain the development probability of the temperature cell is as follows:
where γ is a random number with a value between 0 and 1, α is a parameter controlling the degree of randomness, con ij Is a function that determines whether the cell ij can be switched, and the cell value is set to 0 when the temperature data is not suitable for developing into a temperature cell.
The embodiment of the invention also provides a body temperature data processing device, which comprises:
the influence factor generation module is used for acquiring the body temperature data of the user, determining elements influencing the accuracy of the body temperature after spatial registration interpretation processing is carried out on the data, and forming influence factors;
the grid distance generation module is used for establishing a three-dimensional coordinate and a standard grid, prescribing the influence factors in the standard grid in the three-dimensional coordinate, calculating the distance of the standard grid to each influence factor, and then carrying out normalization processing to generate grid distance data with the size consistent with that of the standard grid;
the development probability generation module is used for obtaining the weight of each influence factor through algorithm calculation, and calculating the development probability of the temperature cells according to the weight value of each influence factor and by combining the grid density and the influence factors;
and the accurate data generation module is used for calculating the development probability of the temperature cells and the acquired body temperature data of the user to obtain the deduced accurate data.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; wherein the computer program, when running, controls the device on which the computer readable storage medium is located to perform the body temperature data processing method according to any one of the above.
An embodiment of the present invention further provides a terminal device, which is characterized by including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor, when executing the computer program, implements the body temperature data processing method according to any one of the above items.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the invention carries out analog evolution on the temperature data through the cellular automata technology, fits accurate data, solves the technical problem of inaccurate data measurement in the existing thermal induction non-contact body temperature detection, and realizes the improvement of the accuracy of the thermal induction non-contact body temperature detection method.
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FIG. 1: is a flow chart of the method steps in the embodiment of the invention;
FIG. 2: is a connection diagram of the device structure in the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a preferred embodiment of the present invention provides a body temperature data processing method, including:
s1, obtaining body temperature data of a user, performing spatial registration interpretation processing, and determining elements influencing body temperature accuracy to form influence factors;
s2, establishing a three-dimensional coordinate and a standard grid, defining the influence factors in the standard grid in the three-dimensional coordinate, calculating the distance of the standard grid to each influence factor, and then performing normalization processing to generate grid distance data with the same size as the standard grid;
in this embodiment, after calculating the distance between the standard grid and each influence factor, performing normalization processing to generate grid distance data having a size consistent with that of the standard grid, includes: normalizing the acquired influence factor data, uniformly resampling the data to be consistent resolution, and calculating the space distance from the temperature cell to each influence factor according to Euclidean measurement, wherein the calculation formula is as follows:
in the formula (x) 0 ，y 0 ) Is the coordinate position of the cell (x) k ，y k ) Is the coordinate position of the impact factor, dis is the calculated Euclidean distance.
S3, calculating the weight of each influence factor through an algorithm, and calculating the development probability of the temperature cells according to the weight value of each influence factor and the grid density and the influence factors;
and S4, calculating the development probability of the temperature cells and the acquired body temperature data of the user to obtain deduced accurate data.
In this embodiment, the weight algorithm for obtaining each influence factor through calculation is a hierarchical analysis algorithm.
In this embodiment, the obtaining the weight of each influence factor through algorithm calculation, and calculating the development probability of the temperature unit according to the weight of each influence factor and by combining the grid density and the influence factors includes: carrying out weighted summation on each influence factor through the weight values of the influence factors to calculate and develop suitability data; calculating the cell development density of the neighborhood of the central cell according to the number of the cells around the central cell and the density of the standard grid; and multiplying the development suitability data and the cellular suitability data to obtain the development probability of the temperature cellular.
In this embodiment, the formula for calculating the development suitability data by weighted summation of the weight values of the influence factors is as follows:
p g ＝b 1 x 1 +b 2 x 2 +…+b k x k +…+b n x n
in the formula, x k Is the k-th space variable, b k Is a variable x k The weight of (a) is calculated by an analytic hierarchy process, p g For the development suitability of the cells, n is the number of influencing factors.
In this embodiment, the formula for calculating the cell development density of the neighborhood of the central cell according to the number of cells around the central cell and the density of the standard grid is as follows:
in the formula (I), the compound is shown in the specification,
counting the number of cells which have been converted into temperature cells in a 5 × 5 neighborhood of the ith row and jth column of cells ij at time t, wherein 5 × 5 is the length 5 multiplied by the width 5 of a coordinate space region of the standard grid,
density is developed for the cells.
In this embodiment, the formula for obtaining the development probability of the temperature cell by multiplying the development suitability data and the cell suitability data is as follows:
where γ is a random number having a value between 0 and 1, α is a parameter controlling the degree of randomness, con ij Is a function that determines whether a cell ij can be transformed, and when the temperature data is not suitable for developing into a temperature cell, the cell value is set to 0.
Accordingly, referring to fig. 2, a body temperature data processing device according to a preferred embodiment of the present invention includes:
the influence factor generation module is used for acquiring the body temperature data of the user, determining elements influencing the accuracy of the body temperature after spatial registration interpretation processing is carried out on the data, and forming influence factors;
the grid distance generation module is used for establishing a three-dimensional coordinate and a standard grid, prescribing the influence factors in the standard grid in the three-dimensional coordinate, calculating the distance of the standard grid to each influence factor, and then carrying out normalization processing to generate grid distance data with the size consistent with that of the standard grid;
the development probability generation module is used for obtaining the weight of each influence factor through algorithm calculation, and calculating the development probability of the temperature cells according to the weight value of each influence factor and by combining the grid density and the influence factors;
and the accurate data generation module is used for calculating the development probability of the temperature cells and the acquired body temperature data of the user to obtain deduced accurate data.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; wherein the computer program, when running, controls the device on which the computer-readable storage medium is located to execute the body temperature data processing method according to any of the above embodiments.
An embodiment of the present invention further provides a terminal device, where the terminal device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the body temperature data processing method according to any of the above embodiments when executing the computer program.
Preferably, the computer program may be divided into one or more modules/units (e.g., computer program) that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc., the general purpose Processor may be a microprocessor, or the Processor may be any conventional Processor, the Processor is a control center of the terminal device, and various interfaces and lines are used to connect various parts of the terminal device.
The memory mainly includes a program storage area that may store an operating system, an application program required for at least one function, and the like, and a data storage area that may store related data and the like. In addition, the memory may be a high speed random access memory, may also be a non-volatile memory, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), and the like, or may also be other volatile solid state memory devices.
It should be noted that the terminal device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the terminal device is only an example and does not constitute a limitation of the terminal device, and may include more or less components, or combine some components, or different components.
The invention carries out analog evolution on the temperature data through the cellular automata technology, fits accurate data, solves the technical problem of inaccurate data measurement in the existing thermal induction non-contact body temperature detection, and realizes the improvement of the accuracy of the thermal induction non-contact body temperature detection method.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.
Claims (10)
1. A body temperature data processing method, comprising:
acquiring body temperature data of a user, performing spatial registration interpretation processing, and determining elements influencing body temperature accuracy to form influencing factors;
establishing a three-dimensional coordinate and a standard grid, specifying the influence factors in the standard grid in the three-dimensional coordinate, calculating the distance of the standard grid to each influence factor, and then performing normalization processing to generate grid distance data with the same size as the standard grid;
calculating the weight of each influence factor through an algorithm, and calculating the development probability of the temperature cells according to the weight value of each influence factor and by combining the grid density and the influence factors;
and calculating the development probability of the temperature cells and the acquired body temperature data of the user to obtain deduced accurate data.
2. The body temperature data processing method according to claim 1, wherein after calculating the distance of the standard grid to each influence factor, performing normalization processing to generate grid distance data having a size consistent with that of the standard grid, comprises: normalizing the acquired influence factor data, uniformly resampling the data to consistent resolution, and calculating the spatial distance from the temperature cell to each influence factor according to Euclidean measurement, wherein the calculation formula is as follows:
wherein (x) 0 ，y 0 ) Is the coordinate position of the cell (x) k ，y k ) Is the coordinate position of the impact factor, dis is the calculated Euclidean distance.
3. The method for processing body temperature data according to claim 1, wherein the weight algorithm for calculating each influence factor is a hierarchical analysis algorithm.
4. The method for processing body temperature data according to claim 3, wherein the calculating by an algorithm to obtain the weight of each influence factor, and calculating the development probability of the temperature cells according to the weight of each influence factor and the grid density and the influence factors comprises:
carrying out weighted summation on each influence factor through the weight values of the influence factors to calculate and develop suitability data;
calculating the cell development density of the neighborhood of the central cell according to the number of cells around the central cell and the density of the standard grid;
and multiplying the development suitability data and the cellular suitability data to obtain the development probability of the temperature cellular.
5. The method for processing body temperature data according to claim 4, wherein the formula for calculating the development suitability data by weighted summation of the weight values of the influence factors is as follows:
p g ＝b 1 x 1 +b 2 x 2 +…+b k x k +…+b n x n
in the formula, x k Is the k-th space variable, b k Is a variable x k The weight of (a) is calculated by an analytic hierarchy process, p g For the development suitability of the cells, n is the number of influencing factors.
6. The method for processing body temperature data according to claim 4, wherein the formula for calculating the cell development density of the neighborhood of the central cell from the number of cells around the central cell in combination with the density of the standard grid is:
in the formula (I), the compound is shown in the specification,
counting the number of cells which have been converted into temperature cells in a 5 × 5 neighborhood of the ith row and jth column of cells ij at time t, wherein 5 × 5 is the length 5 multiplied by the width 5 of a coordinate space region of the standard grid,
density is developed for the cells.
7. The body temperature data processing method according to claim 4, wherein the formula for multiplying the development suitability data and the cellular suitability data to obtain the probability of development of the temperature cell is:
where γ is a random number with a value between 0 and 1, α is a parameter controlling the degree of randomness, con ij Is a function that determines whether a cell ij can be transformed, and when the temperature data is not suitable for developing into a temperature cell, the cell value is set to 0.
8. A body temperature data processing device, comprising:
the influence factor generation module is used for acquiring the body temperature data of the user, determining elements influencing the accuracy of the body temperature after spatial registration interpretation processing is carried out on the data, and forming influence factors;
the grid distance generation module is used for establishing a three-dimensional coordinate and a standard grid, prescribing the influence factors in the standard grid in the three-dimensional coordinate, calculating the distance of the standard grid to each influence factor, and then carrying out normalization processing to generate grid distance data with the size consistent with that of the standard grid;
the development probability generation module is used for obtaining the weight of each influence factor through algorithm calculation, and calculating the development probability of the temperature cells according to the weight value of each influence factor and by combining the grid density and the influence factors;
and the accurate data generation module is used for calculating the development probability of the temperature cells and the acquired body temperature data of the user to obtain the deduced accurate data.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored computer program; wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the body temperature data processing method according to any one of claims 1 to 7.
10. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the body temperature data processing method according to any one of claims 1 to 7 when executing the computer program.
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